{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd7fec99-68b4-4646-a2cd-2e2045424678",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Travelling Salesman Problem\n",
    "\n",
    "## Introduction\n",
    "\n",
    "The \"Travelling Salesperson Problem\" [[1](#TSPWiki)] refers to finding the shortest route between cities, given their relative distances. In a more general sense, given a weighted directed graph, one shall find the shortest route along the graph that goes through all the cities, where the weights correspond to the distance between cities. For example, in the following graph\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "51df6235-e46c-45d0-ba63-505f203e5183",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:03:57.679317Z",
     "iopub.status.busy": "2024-05-07T15:03:57.678151Z",
     "iopub.status.idle": "2024-05-07T15:03:59.103250Z",
     "shell.execute_reply": "2024-05-07T15:03:59.102599Z"
    },
    "lines_to_next_cell": 2,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx  # noqa\n",
    "\n",
    "nonedge = 5\n",
    "graph = nx.DiGraph()\n",
    "graph.add_nodes_from([0, 1, 2, 3])\n",
    "graph.add_edges_from([(0, 1), (1, 2), (2, 1), (2, 3)], weight=1)\n",
    "graph.add_edges_from([(0, 2), (1, 3)], weight=2)\n",
    "pos = nx.planar_layout(graph)\n",
    "nx.draw_networkx(graph, pos=pos)\n",
    "\n",
    "labels = nx.get_edge_attributes(graph, \"weight\")\n",
    "nx.draw_networkx_edge_labels(graph, pos, edge_labels=labels)\n",
    "distance_matrix = nx.convert_matrix.to_numpy_array(graph, nonedge=nonedge)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28baf985-aea6-4b00-95ab-fd091206c7f1",
   "metadata": {
    "tags": []
   },
   "source": [
    "one can see that the route which goes along $0\\rightarrow 1\\rightarrow 2\\rightarrow 3$ yields a total distance 3, which is the shortest.\n",
    "\n",
    "As many other real world problems, this task may be cast into a combinatorial optimization problem. In this demo, we will show how the Quantum Approximate Optimization Algorithm [[2](#QAOA)] can be employed on the Classiq platform to solve the Travelling Salesperson Problem.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "903019b9-044b-4340-821a-3d8b77abae69",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Mathematical formulation\n",
    "\n",
    "As a first step, we have to model the problem mathematically. The input is the set of distances between the cities: this is given by a matrix $w$, whose $(i,j)$ entry refers to the distance between city $i$ to city $j$. The output of the model is an optimized route. Any route can be captured by a binary matrix $x$ which states at each step (row) which city was visited (column):\n",
    "$\\begin{aligned}\n",
    "x_{ij} =\n",
    "\\begin{cases}\n",
    "      1 & \\text{at time step } i \\text{ the route is in city } j \\\\\n",
    "      0 & \\text{else}\n",
    "\\end{cases}\\\\\n",
    "\\end{aligned}$\n",
    "\n",
    "For example:\n",
    "$\\begin{aligned}\n",
    "x=\\begin{pmatrix}\n",
    "0 & 1 & 0 & 0 \\\\\n",
    "0 & 0 & 0 & 1 \\\\\n",
    "0 & 0 & 1 & 0 \\\\\n",
    "1 & 0 & 0 & 0\n",
    "\\end{pmatrix}\n",
    "\\end{aligned}$ means that we start from city 1 go to city 3 then to city 2 and end at city 0.\n",
    "\n",
    "#### The constrained optimization problem is thus defined as follows:\n",
    "\n",
    "- find x, that minimizes the path distance -\n",
    "  $\\begin{aligned}\n",
    "\\min_{x_{i, p} \\in \\{0, 1\\}}  \\Sigma_{i, j} w_{i, j} \\Sigma_p x_{i, p} x_{j, p + 1}\\\\\n",
    "\\end{aligned}$\n",
    "\n",
    "(note that the inner sum over $p$ is simply an indicator for whether we go from city $i$ to city $j$)\n",
    "\n",
    "such that:\n",
    "\n",
    "- each point is visited once -\n",
    "  $\\begin{aligned}\n",
    "\\forall i, \\hspace{0.2cm} \\Sigma_p x_{i, p} = 1\\\\\n",
    "\\end{aligned}$\n",
    "\n",
    "- in each step only a single point is visited -\n",
    "  $\\begin{aligned}\n",
    "\\forall p, \\hspace{0.2cm} \\Sigma_i x_{i, p} = 1\\\\\n",
    "\\end{aligned}$\n",
    "\n",
    "#### Directed graph\n",
    "\n",
    "In some cases, as in the graph above, not all cities are connected, and it is more proper to describe the problem with a weighted, directed graph. In that case, since we would like to find the shortest path, unconnected cities are assumed to have an infinite distance between them. For example, the graph above corresponds to the matrix\n",
    "\n",
    "$\\begin{aligned}\n",
    "w=\\begin{pmatrix}\n",
    "\\infty & 1 & 2 & \\infty \\\\\n",
    "\\infty & \\infty & 1 & 2 \\\\\n",
    "\\infty & 1 & \\infty & 1 \\\\\n",
    "\\infty & \\infty & \\infty & \\infty\n",
    "\\end{pmatrix}\n",
    "\\end{aligned}$\n",
    "\n",
    "In practice we will choose a large enough weight rather than infinity.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8d2493-525d-4aa9-8b93-dd10263a4749",
   "metadata": {},
   "source": [
    "# Solving with the Classiq platform\n",
    "\n",
    "We go through the steps of solving the problem with the Classiq platform, using QAOA algorithm. The solution is based on defining a pyomo model for the optimization problem we would like to solve.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66dfdc09-4920-4966-a738-d8a8e7b4dc4e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Building the Pyomo model from a matrix of distances input\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd946990-d0dc-4f29-aa15-949819b8d09f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:03:59.108007Z",
     "iopub.status.busy": "2024-05-07T15:03:59.106827Z",
     "iopub.status.idle": "2024-05-07T15:03:59.261041Z",
     "shell.execute_reply": "2024-05-07T15:03:59.260218Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from typing import List, Tuple, cast  # noqa\n",
    "\n",
    "import numpy as np  # noqa\n",
    "import pyomo.core as pyo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c731afb0-f94b-43b1-9cdb-1de8bb3a1f46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:03:59.266490Z",
     "iopub.status.busy": "2024-05-07T15:03:59.265242Z",
     "iopub.status.idle": "2024-05-07T15:03:59.275592Z",
     "shell.execute_reply": "2024-05-07T15:03:59.274793Z"
    },
    "lines_to_next_cell": 2,
    "tags": []
   },
   "outputs": [],
   "source": [
    "## We define a function which gets the matrix of distances and returns a Pyomo model\n",
    "\n",
    "\n",
    "def PyomoTSP(dis_mat: np.ndarray) -> pyo.ConcreteModel:\n",
    "    model = pyo.ConcreteModel(\"TSP\")\n",
    "\n",
    "    assert dis_mat.shape[0] == dis_mat.shape[1], \"error distance matrix is not square\"\n",
    "\n",
    "    NofCities = dis_mat.shape[0]  # total number of cities\n",
    "    cities = range(NofCities)  # list of cities\n",
    "\n",
    "    # we define our variable, which is the binary matrix x: x[i, j] = 1 indicates that point i is visited at step j\n",
    "    model.x = pyo.Var(cities, cities, domain=pyo.Binary)\n",
    "\n",
    "    # we add constraints\n",
    "    @model.Constraint(cities)\n",
    "    def each_step_visits_one_point_rule(model, ii):\n",
    "        return sum(model.x[ii, jj] for jj in range(NofCities)) == 1\n",
    "\n",
    "    @model.Constraint(cities)\n",
    "    def each_point_visited_once_rule(model, jj):\n",
    "        return sum(model.x[ii, jj] for ii in range(NofCities)) == 1\n",
    "\n",
    "    # we define our Objective function\n",
    "    def is_connected(i1: int, i2: int):\n",
    "        return sum(model.x[i1, kk] * model.x[i2, kk + 1] for kk in cities[:-1])\n",
    "\n",
    "    model.cost = pyo.Objective(\n",
    "        expr=sum(\n",
    "            dis_mat[i1, i2] * is_connected(i1, i2) for i1, i2 in model.x.index_set()\n",
    "        )\n",
    "    )\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a39488-ddbd-4e1a-930d-5a9c1b307185",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Generating a specific problem\n",
    "\n",
    "Let us pick a specific problem: the graph introduced above.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f860b99c-61cd-4071-964b-6971570b5fc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:03:59.280553Z",
     "iopub.status.busy": "2024-05-07T15:03:59.279354Z",
     "iopub.status.idle": "2024-05-07T15:03:59.608392Z",
     "shell.execute_reply": "2024-05-07T15:03:59.607689Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We generate a graph which defines our problem\n",
    "import networkx as nx  # noqa\n",
    "\n",
    "graph = nx.DiGraph()\n",
    "graph.add_nodes_from([0, 1, 2, 3])\n",
    "graph.add_edges_from([(0, 1), (1, 2), (2, 1), (2, 3)], weight=1)\n",
    "graph.add_edges_from([(0, 2), (1, 3)], weight=2)\n",
    "pos = nx.planar_layout(graph)\n",
    "nx.draw_networkx(graph, pos=pos)\n",
    "\n",
    "labels = nx.get_edge_attributes(graph, \"weight\")\n",
    "nx.draw_networkx_edge_labels(graph, pos, edge_labels=labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "236ed658-b452-4bf9-bea9-a75f7e80c478",
   "metadata": {},
   "source": [
    "We convert the graph object into a matrix of distances and then generate a pyomo model for this example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38a2d5cc-fd5e-4f84-9eee-67a5133daf29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:03:59.613341Z",
     "iopub.status.busy": "2024-05-07T15:03:59.612110Z",
     "iopub.status.idle": "2024-05-07T15:03:59.621533Z",
     "shell.execute_reply": "2024-05-07T15:03:59.620823Z"
    },
    "lines_to_next_cell": 2,
    "tags": []
   },
   "outputs": [],
   "source": [
    "nonedge = 5  # this variable refers to how much we penalize for unconnected points\n",
    "distance_matrix = nx.convert_matrix.to_numpy_array(graph, nonedge=nonedge)\n",
    "tsp_model = PyomoTSP(distance_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a645544e-b083-4d03-9b1b-9348c80c3cbb",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setting Up the Classiq Problem Instance\n",
    "\n",
    "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f52a988f-5b72-4faa-b122-6099113fb60e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:03:59.626463Z",
     "iopub.status.busy": "2024-05-07T15:03:59.625285Z",
     "iopub.status.idle": "2024-05-07T15:04:01.558451Z",
     "shell.execute_reply": "2024-05-07T15:04:01.557758Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import construct_combinatorial_optimization_model\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad8e668b-eb6b-4d2c-ba85-16a0ea519731",
   "metadata": {},
   "source": [
    "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "970d4e26-0bf0-489a-94cf-e26fef47e89f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:04:01.562375Z",
     "iopub.status.busy": "2024-05-07T15:04:01.561788Z",
     "iopub.status.idle": "2024-05-07T15:04:01.567916Z",
     "shell.execute_reply": "2024-05-07T15:04:01.567289Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "optimizer_config = OptimizerConfig(max_iteration=50, alpha_cvar=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff2a1af-b0d8-44fe-9e95-fb0b151e5818",
   "metadata": {},
   "source": [
    "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb125a5d-13ec-451b-bcd7-64e49df69bc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:04:01.572363Z",
     "iopub.status.busy": "2024-05-07T15:04:01.571243Z",
     "iopub.status.idle": "2024-05-07T15:04:02.608885Z",
     "shell.execute_reply": "2024-05-07T15:04:02.608083Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=tsp_model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c1f31ad-e71b-473f-acb5-2b49d2230410",
   "metadata": {},
   "source": [
    "We also set the quantum backend we want to execute on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bf05d235-d298-438e-beb0-be1175fcd982",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:04:02.614109Z",
     "iopub.status.busy": "2024-05-07T15:04:02.612854Z",
     "iopub.status.idle": "2024-05-07T15:04:02.691169Z",
     "shell.execute_reply": "2024-05-07T15:04:02.690363Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import set_execution_preferences\n",
    "from classiq.execution import ClassiqBackendPreferences, ExecutionPreferences\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"aer_simulator\")\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, backend_preferences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a72ae181-d30d-4435-aea6-484b49217640",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:04:02.696305Z",
     "iopub.status.busy": "2024-05-07T15:04:02.695106Z",
     "iopub.status.idle": "2024-05-07T15:04:02.819142Z",
     "shell.execute_reply": "2024-05-07T15:04:02.818420Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import write_qmod\n",
    "\n",
    "write_qmod(qmod, \"traveling_saleman_problem\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80bb20df-ca97-402f-9083-8b500612ae2a",
   "metadata": {},
   "source": [
    "## Synthesizing the QAOA Circuit and Solving the Problem\n",
    "\n",
    "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "33a41523-99e2-4669-bf55-2d19b66f4f2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:04:02.824219Z",
     "iopub.status.busy": "2024-05-07T15:04:02.823027Z",
     "iopub.status.idle": "2024-05-07T15:04:29.138440Z",
     "shell.execute_reply": "2024-05-07T15:04:29.137755Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: https://platform.classiq.io/circuit/c7a7f06e-213e-4a50-99d9-d5032079b34b?version=0.41.0.dev39%2B79c8fd0855\n"
     ]
    }
   ],
   "source": [
    "from classiq import show, synthesize\n",
    "\n",
    "qprog = synthesize(qmod)\n",
    "show(qprog)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b696e21-8caf-4f14-b345-88771d052dc0",
   "metadata": {},
   "source": [
    "We now solve the problem by calling the `execute` function on the quantum program we have generated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1bf1079f-a37c-4b08-a7a8-53f675570a23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:04:29.140876Z",
     "iopub.status.busy": "2024-05-07T15:04:29.140511Z",
     "iopub.status.idle": "2024-05-07T15:05:43.057714Z",
     "shell.execute_reply": "2024-05-07T15:05:43.057070Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import execute\n",
    "\n",
    "res = execute(qprog).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc744bff-31cf-49f8-a94d-b4b7235e18bb",
   "metadata": {},
   "source": [
    "We can check the convergence of the run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "146044ba-d173-4f75-8715-2deeb7813a4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:05:43.060390Z",
     "iopub.status.busy": "2024-05-07T15:05:43.060025Z",
     "iopub.status.idle": "2024-05-07T15:05:43.082714Z",
     "shell.execute_reply": "2024-05-07T15:05:43.082089Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classiq.execution import VQESolverResult\n",
    "\n",
    "vqe_result = res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "051a5b91-d17c-4477-a416-6bfd47032954",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Optimization Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed429305-36cd-4703-8993-15568d7fd44d",
   "metadata": {},
   "source": [
    "We can also examine the statistics of the algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "31c4f692-2847-4232-8ca5-6253ff0b797d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:05:43.086056Z",
     "iopub.status.busy": "2024-05-07T15:05:43.085666Z",
     "iopub.status.idle": "2024-05-07T15:05:43.532932Z",
     "shell.execute_reply": "2024-05-07T15:05:43.531306Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cost</th>\n",
       "      <th>solution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>0.001</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.003</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>0.001</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.001</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.003</td>\n",
       "      <td>5.0</td>\n",
       "      <td>[1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     probability  cost                                          solution  \\\n",
       "413        0.001   5.0  [1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0]   \n",
       "38         0.003   5.0  [0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]   \n",
       "364        0.001   5.0  [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0]   \n",
       "197        0.001   5.0  [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0]   \n",
       "43         0.003   5.0  [1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]   \n",
       "\n",
       "     count  \n",
       "413      1  \n",
       "38       3  \n",
       "364      1  \n",
       "197      1  \n",
       "43       3  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from classiq.applications.combinatorial_optimization import (\n",
    "    get_optimization_solution_from_pyo,\n",
    ")\n",
    "\n",
    "solution = get_optimization_solution_from_pyo(\n",
    "    tsp_model, vqe_result=vqe_result, penalty_energy=qaoa_config.penalty_energy\n",
    ")\n",
    "optimization_result = pd.DataFrame.from_records(solution)\n",
    "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34a15955-0a09-481c-a997-ecd3ffa09a44",
   "metadata": {},
   "source": [
    "And the histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "786019a7-5c89-4fd2-90bc-da4e4aa42214",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:05:43.537441Z",
     "iopub.status.busy": "2024-05-07T15:05:43.536303Z",
     "iopub.status.idle": "2024-05-07T15:05:43.818449Z",
     "shell.execute_reply": "2024-05-07T15:05:43.817579Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'cost'}>]], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a103578-5993-4d08-94f1-8cc93973b0df",
   "metadata": {},
   "source": [
    "Lastly, we can compare to the classical solution of the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c309c6c1-a494-4ee1-88cb-5a1d8a582d4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:05:43.823347Z",
     "iopub.status.busy": "2024-05-07T15:05:43.822144Z",
     "iopub.status.idle": "2024-05-07T15:05:43.972968Z",
     "shell.execute_reply": "2024-05-07T15:05:43.972243Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model TSP\n",
      "\n",
      "  Variables:\n",
      "    x : Size=16, Index=x_index\n",
      "        Key    : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "        (0, 0) :     0 :   1.0 :     1 : False : False : Binary\n",
      "        (0, 1) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (0, 2) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (0, 3) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (1, 0) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (1, 1) :     0 :   1.0 :     1 : False : False : Binary\n",
      "        (1, 2) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (1, 3) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (2, 0) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (2, 1) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (2, 2) :     0 :   1.0 :     1 : False : False : Binary\n",
      "        (2, 3) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (3, 0) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (3, 1) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (3, 2) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (3, 3) :     0 :   1.0 :     1 : False : False : Binary\n",
      "\n",
      "  Objectives:\n",
      "    cost : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Value\n",
      "        None :   True :   3.0\n",
      "\n",
      "  Constraints:\n",
      "    each_step_visits_one_point_rule : Size=4\n",
      "        Key : Lower : Body : Upper\n",
      "          0 :   1.0 :  1.0 :   1.0\n",
      "          1 :   1.0 :  1.0 :   1.0\n",
      "          2 :   1.0 :  1.0 :   1.0\n",
      "          3 :   1.0 :  1.0 :   1.0\n",
      "    each_point_visited_once_rule : Size=4\n",
      "        Key : Lower : Body : Upper\n",
      "          0 :   1.0 :  1.0 :   1.0\n",
      "          1 :   1.0 :  1.0 :   1.0\n",
      "          2 :   1.0 :  1.0 :   1.0\n",
      "          3 :   1.0 :  1.0 :   1.0\n"
     ]
    }
   ],
   "source": [
    "from pyomo.opt import SolverFactory\n",
    "\n",
    "solver = SolverFactory(\"couenne\")\n",
    "solver.solve(tsp_model)\n",
    "\n",
    "tsp_model.display()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f3d6b0-5de9-42c9-b52a-4c31ef2d30a7",
   "metadata": {},
   "source": [
    "If we get the right solution we plot it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e3ce696d-9534-44f8-b57d-5694b6132e32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:05:43.978058Z",
     "iopub.status.busy": "2024-05-07T15:05:43.976763Z",
     "iopub.status.idle": "2024-05-07T15:05:43.982453Z",
     "shell.execute_reply": "2024-05-07T15:05:43.981762Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "best_classical_solution = np.array(\n",
    "    [int(pyo.value(tsp_model.x[idx])) for idx in np.ndindex(distance_matrix.shape)]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aa902ddb-a27c-4137-825e-c7e5173659a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:05:43.987182Z",
     "iopub.status.busy": "2024-05-07T15:05:43.986025Z",
     "iopub.status.idle": "2024-05-07T15:05:43.991602Z",
     "shell.execute_reply": "2024-05-07T15:05:43.990907Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "best_quantum_solution = np.array(\n",
    "    optimization_result.solution[optimization_result.cost.idxmin()]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4c97d27d-c76f-44a2-9e6b-a1e43d78a286",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:05:43.996166Z",
     "iopub.status.busy": "2024-05-07T15:05:43.994975Z",
     "iopub.status.idle": "2024-05-07T15:05:44.003151Z",
     "shell.execute_reply": "2024-05-07T15:05:44.002368Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if (best_classical_solution == best_quantum_solution).all():\n",
    "    routesol = np.zeros(4, dtype=int)\n",
    "    for k in range(4):\n",
    "        routesol[k] = np.where(\n",
    "            best_quantum_solution.reshape(distance_matrix.shape)[k, :] == 1\n",
    "        )[0]\n",
    "    edgesol = list(nx.utils.pairwise(routesol))\n",
    "    nx.draw_networkx(\n",
    "        graph,\n",
    "        pos,\n",
    "        with_labels=True,\n",
    "        edgelist=edgesol,\n",
    "        edge_color=\"red\",\n",
    "        node_size=200,\n",
    "        width=3,\n",
    "    )\n",
    "    nx.draw_networkx(graph, pos=pos)\n",
    "\n",
    "    labels = nx.get_edge_attributes(graph, \"weight\")\n",
    "    nx.draw_networkx_edge_labels(graph, pos, edge_labels=labels)\n",
    "    print(\"The route of the traveller is:\", routesol)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28fdd4b2-7aad-43ca-a3d0-bb32df703d16",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "\n",
    "## References\n",
    "\n",
    "<a id='TSPWiki'>[1]</a>: [Travelling Salesperson Problem (Wikipedia)](https://en.wikipedia.org/wiki/Travelling_salesman_problem)\n",
    "\n",
    "<a id='QAOA'>[2]</a>: [Farhi, Edward, Jeffrey Goldstone, and Sam Gutmann. \"A quantum approximate optimization algorithm.\" arXiv preprint arXiv:1411.4028 (2014).](https://arxiv.org/abs/1411.4028)\n",
    "\n",
    "<a id='cvar'>[3]</a>: [Barkoutsos, Panagiotis Kl, et al. \"Improving variational quantum optimization using CVaR.\" Quantum 4 (2020): 256.](https://arxiv.org/abs/1907.04769)\n"
   ]
  }
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